Built a scalable DataOps pipeline for a major e-commerce platform, processing 10TB of daily data and reducing time-to-insight by 50%.
10TB in 4 hours
Data Processing Speed
70% faster
2 hours
Time to Insights
50% reduction
99.2%
Data Quality Score
40% improvement
The Challenge
GlobalMart Enterprise struggled with processing massive volumes of customer and transaction data, leading to delayed business insights and poor decision-making capabilities.
Processing 10TB+ of daily data with legacy systems
Data quality issues affecting analytics accuracy
Siloed data sources preventing unified insights
Slow data pipeline causing delayed reporting
Inconsistent data formats across systems
Limited real-time analytics capabilities
Our Approach
Implemented modern DataOps platform with real-time processing, automated quality checks, and self-service analytics capabilities.
Apache KafkaSparkAirflowSnowflakedbtGreat ExpectationsTableau
Implementation Timeline
Total Duration: 20 weeks implementation
1
Data Architecture Design
4 weeks
- Current state data mapping
- Target architecture design
- Technology selection and planning
- Data governance framework setup
2
Infrastructure Setup
6 weeks
- Cloud data platform deployment
- Data ingestion pipeline development
- Storage and compute optimization
- Security and access control implementation
3
Data Pipeline Development
6 weeks
- ETL/ELT pipeline automation
- Data quality monitoring setup
- Real-time processing implementation
- Data transformation and modeling
4
Analytics & Visualization
4 weeks
- Self-service analytics platform setup
- Dashboard and report automation
- User training and adoption
- Performance optimization
Technical Architecture
Cloud-native data platform with streaming ingestion, automated processing, and self-service analytics capabilities.
Kafka for real-time data streaming
Spark for distributed data processing
Snowflake for cloud data warehousing
Airflow for workflow orchestration
dbt for data transformation
Great Expectations for data quality
Results & Impact
10TB in 4 hours
Data Processing Speed
70% faster
2 hours
Time to Insights
50% reduction
99.2%
Data Quality Score
40% improvement
Sub-second
Real-time Analytics
Near real-time capability
60% reduction
Cost per TB
$2.1M annual savings
Business Benefits
Faster business decision making
Improved customer experience through real-time personalization
Enhanced operational efficiency
Better inventory management
Increased revenue through data-driven insights
Reduced infrastructure costs
“The DataOps transformation has revolutionized how we use data at GlobalMart. We now have real-time insights that drive immediate business decisions, resulting in a 15% increase in conversion rates.”
Lisa Wang
VP of Data & Analytics, GlobalMart Enterprise
Key Learnings
Data quality automation is essential for reliable analytics
Real-time processing capabilities drive competitive advantage
Self-service analytics empowers business users
Proper data governance prevents future technical debt
Recommendations
Invest in automated data quality monitoring from the start
Design for scalability and future growth
Implement proper data lineage and documentation
Focus on user adoption and training for analytics tools
DataOpsBig DataE-commerceReal-time AnalyticsCloud